Application of Non-Linear Gaussian Regression-Based Adaptive Clock Synchronization Technique for Wireless Sensor Network in Agriculture

Upadhyay, Divya; Dubey, Ashwani Kumar; Thilagam, P. Santhi

doi:10.1109/jsen.2018.2818302

Cited by 24 publications

(15 citation statements)

References 29 publications

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“…In equation 8 Suppose that hypothesis F(t )  0, where t is unpredictable, is true (if  t is predictable, experts will recompose the equation (10), so that the error will become unpredictable).…”

Section: Formation Mechanism Of Rational Expectations Modelmentioning

confidence: 99%

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Digital models of naive, adaptive and rational expectations of regional agriculture forecasting

Timofeeva¹

2019

Proceedings of the International Scientific and Practical Conference “Digital Agriculture - Development Strategy” (ISPC 2019)

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The development of agriculture in Russia is a high-priority task. Implementation of "Digitalization of Agriculture" program by 2021 will allow solving a number of problems existing in this industry (food security of the country, export potential increase, etc.) and creating a high-tech branch. One of the directions of "Digitalization of Agriculture" program at the regional level is "smart sectoral planning" which will allow optimizing farmer's activities when making a decision under the conditions of limited time. Currently, digitalization of agriculture in our country is at a low level. There is a lack of scientific and practical knowledge on technologies and methods in this field, a low level of forecast prices for agricultural products, etc. Let us consider the issue of adaptive forecasting in agricultural sector. The real processes in this industry take place in changing conditions, so the economic system has to adapt to them. The model describing economic system behavior will constantly adapt to the economic process. Adaptive forecasting methods allow building a class of self-adjusting models of economic systems that are able to reflect dynamic, changing over time processes in regional agricultural sector, to highlight the most significant parameters and to give fairly accurate forecasts of processes in future. The goal of this paper is to study using of adaptive methods for forecasting in the agricultural sector of the region. Using the data on the agricultural sector efficiency in certain region (Lipetsk Region), we show the mechanism of action of naive, adaptive and rational expectations models. As economical time series, statistical data were used on agricultural sector efficiency in the Lipetsk regionprofitability level, parameters for output technological efficiency. As a result of studying the mechanism of action of naive, adaptive and rational models, conclusions were drawn about the feasibility of the wide acceptance of adaptive method for forecasting in the agricultural sector of the region as one of the factors for digital agriculture development. Keywords-adaptive model, naive expectations model, rational model, profitability level of agricultural products, parameters of technological efficiency of agricultural production. I.

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Section: Formation Mechanism Of Rational Expectations Modelmentioning

confidence: 99%

“…Later, this field of expertise was developed by P.R. Winters Katsoulacos, 2014) [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Digital models of naive, adaptive and rational expectations of regional agriculture forecasting

Timofeeva¹

2019

Proceedings of the International Scientific and Practical Conference “Digital Agriculture - Development Strategy” (ISPC 2019)

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“…[Yıldırım et al 2018] propuseram um protocolo de sincronização de relógios que aplica um mecanismo de correção de feedback proporcional e integral nos erros das mensagens de sincronização. [Upadhyay et al 2018] desenvolveram um método de correção gaussiano para as diferenças de offsets entre os relógios dos nós.…”

Section: Trabalhos Relacionadosunclassified

“…O objetivo da sincronização de relógiosé fazer com que todos os nós tenham uma visão comum do tempo. A principal forma de sincronizar relógios de maneira estável e precisaé utilizar um nó como referência e, por meio de troca de mensagens de sincronização, fazer com que todos os nós estimem o valor de relógio deste nó (chamado de raiz) [Maróti et al 2004, Yıldırım et al 2018, Cardoso et al 2017, Huang et al 2014, Lenzen et al 2015, Upadhyay et al 2018. A principal dificuldade com esta abordageḿ e que, conforme se aumenta a escala da rede, os nós que estão a mais de um salto de distância do nó raiz apresentam um erro de sincronização maior.…”

Section: Introductionunclassified

Seleção de Nó Raiz Ótimo para Sincronização Eficiente de Relógios em Redes de Sensores Sem Fio

Xavier¹,

Amorim²,

Delicato³

2018

Anais Do Simpósio Brasileiro De Computação Ubíqua E Pervasiva (SBCUP)

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To reduce clock synchronization error in wireless sensor networks, recent works aimed to optimize network topology. Such approaches presented limited performance since the root node, who provides global clock, may remain being located in unfavorable positions after topological optimization. Moreover, in case of node failures, recuperation is a very costly operation. This work presents two proposals to select network optimal root node: the first is a graphbased approach and the second is a distributed algorithm which it is applied to a failure scenario. Experiments show that selecting optimal root node, rather than optimizing topology, decreases synchronization error close to six times less and distributed approach is able to be robust to failures of the root node.Resumo. Para reduzir o erro de sincronização de relógios em redes de sensores sem fio, trabalhos recentes buscaram otimizar a topologia da rede. Essas abordagens apresentaram desempenho limitado pois o nó raiz, o qual fornece o relógio global, pode continuar mal localizado mesmo após a otimização e em caso de falha, a recuperaçãoé muito custosa. Este trabalho propõe duas abordagens para selecionar o nó raizótimo da rede: a primeiraé baseada em grafos e a segundaé um algoritmo distribuído aplicado em um cenário de falha. Experimentos indicam que, comparadaà otimização de topologia, a seleção do raizótimo reduz cerca de seis vezes o erro de sincronização e que a abordagem distribuidaé capaz de ser resiliente em cenários de falha do nó raiz.

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“…With the development of information collection technologies [9][10][11], the ICTs have been gradually introduced in a greenhouse, and the agricultural WSNs have become classical applications for information gathering. e agricultural WSNs have obtained a great progress [12][13][14]. However, on the one hand, current WSNs are mostly composed of static nodes and constraint of energy charging, so that the monitoring systems lack the flexibility and lifespan [15].…”

Section: Introductionmentioning

confidence: 99%

A Cloud-Assisted Region Monitoring Strategy of Mobile Robot in Smart Greenhouse

Chu

et al. 2019

Mobile Information Systems

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In smart agricultural systems, the macroinformation sensing by adopting a mobile robot with multiple types of sensors is a key step for sustainable development of agriculture. Also, in a region monitoring strategy that meets the real-scene requirements, optimal operation of mobile robots is necessary. In this paper, a cloud-assisted region monitoring strategy of mobile robots in a smart greenhouse is presented. First, a hybrid framework that contains a cloud, a wireless network, and mobile multisensor robots is deployed to monitor a wide-region greenhouse. Then, a novel strategy that contains two phases is designed to ensure valid region monitoring and meet the time constraints of a mobile sensing robot. In the first phase, candidate region monitoring points are selected using the improved virtual forces. In the second phase, a moving path for the mobile node is calculated based on Euclidean distance. Subsequently, the applicability of the proposed strategy is verified by the greenhouse test system. The verification results show that the proposed algorithm has better performance than the conventional methods. The results also demonstrate that, by applying the proposed algorithm, the number of monitoring points and time consumption can reduce, while the valid monitoring region area is enlarged.

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Application of Non-Linear Gaussian Regression-Based Adaptive Clock Synchronization Technique for Wireless Sensor Network in Agriculture

Cited by 24 publications

References 29 publications

Digital models of naive, adaptive and rational expectations of regional agriculture forecasting

Digital models of naive, adaptive and rational expectations of regional agriculture forecasting

Seleção de Nó Raiz Ótimo para Sincronização Eficiente de Relógios em Redes de Sensores Sem Fio

A Cloud-Assisted Region Monitoring Strategy of Mobile Robot in Smart Greenhouse

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